Background

Membrane proteins are important drug targets in many human diseases
and gathering structural information regarding these proteins encourages the
pharmaceutical industry to develop new molecules using structure-based drug
design studies. Specifically, membrane-bound catechol-O-methyltransferase (MBCOMT) is an integral membrane protein
that catalyzes the methylation of catechol substrates and has been linked to
several diseases such as Parkinson’s disease and Schizophrenia. Thereby,
improvements in the clinical outcome of the therapy to these diseases may come
from structure-based drug design where reaching MBCOMT samples in milligram
quantities are crucial for acquiring structural information regarding this
target protein. Therefore, the main aim of this work was to optimize the
temperature, dimethylsulfoxide (DMSO) concentration and the methanol flow-rate
for the biosynthesis of recombinant MBCOMT by Pichia
pastoris bioreactor methanol-induced cultures using artificial
neural networks (ANN).

Results

The optimization trials intended to evaluate MBCOMT expression by
P. pastoris bioreactor cultures led to
the development of a first standard strategy for MBCOMT bioreactor biosynthesis
with a batch growth on glycerol until the dissolved oxygen spike, 3 h of
glycerol feeding and 12 h of methanol induction. The ANN modeling of the
aforementioned fermentation parameters predicted a maximum MBCOMT specific
activity of 384.8 nmol/h/mg of protein at 30°C, 2.9 mL/L/H methanol constant
flow-rate and with the addition of 6% (v/v) DMSO with almost 90% of healthy
cells at the end of the induction phase. These results allowed an improvement of
MBCOMT specific activity of 6.4-fold in comparison to that from the small-scale
biosynthesis in baffled shake-flasks.

Conclusions

The ANN model was able to describe the effects of temperature, DMSO
concentration and methanol flow-rate on MBCOMT specific activity, as shown by
the good fitness between predicted and observed values. This experimental
procedure highlights the potential role of chemical chaperones such as DMSO in
improving yields of recombinant membrane proteins with a different topology than
G-coupled receptors. Finally, the proposed ANN shows that the manipulation of
classic fermentation parameters coupled with the addition of specific molecules
can open and reinforce new perspectives in the optimization of P. pastoris bioprocesses for membrane proteins
biosynthesis.

Membrane proteins (MP) are central to many cellular processes: they are
involved in the uptake and export of diverse charged and uncharged molecules, as
well as mediating the interaction of cells with their environment [1]. As a consequence, they are of prime
importance as drug targets to the pharmaceutical industry [1]. Catechol-O-methyltransferase (COMT, EC 2.1.1.6) is a magnesium-dependent enzyme
that catalyzes the methylation of catechol substrates using S-adenosyl-l-methionine (SAM)
as a methyl donor and yielding, as reaction products, the O-methylated catechol and S-adenosyl-l-homocysteine
[2]. In humans, COMT appears as two
molecular forms, a soluble and a membrane-bound isoform (MBCOMT) that is found
mainly associated with the rough endoplasmic reticulum membrane [2]. Specifically, SCOMT is a nonglycosylated
protein containing 221 amino acid residues and a molecular weight of 24.7 kDa while
MBCOMT has an additional peptide in its amino terminal of 50 amino acid residues and
a molecular weight of 30 kDa [2].This
extra peptide contains a stretch of 21 hydrophobic amino acid residues that
constitute the membrane anchor region [2]. In fact, MBCOMT is an integral membrane protein with the
catalytic portion of the enzyme oriented toward the cytoplasmic side of the membrane
[2]. Recently, MBCOMT has gained a
major importance as therapeutic target due to its high abundance in human brain and
its higher affinity for catechol substrates when compared to soluble isoform
[2]. During the last decades, COMT
has been implicated in several diseases such as cardiovascular diseases
[3], estrogen-induced cancers
[4] and neurologic disorders
[2]. Specifically, the best
documented is the important role that COMT plays in Parkinson’s disease whose most
effective treatment remains the dopamine replacement therapy with levodopa together
with an inhibitor of aromatic amino acid decarboxylase and a COMT inhibitor
[2]. Therefore, it becomes clear the
importance of developing new and more effective drugs for COMT inhibition in which
structure-based drug design can play an important role in this process. However, in
order to structurally and functionally characterize a MP, a stable active sample is
required, meaning the requirement for a regular supply of milligram quantities of
purified MP [1]. The foremost
requirements associated with the majority of biophysical techniques emphasize the
importance of developing new systems capable of delivery biologically active MBCOMT
in higher quantities from high cell-density cultures. Around the mid of the
twentieth century, bacteria and filamentous fungi have taken over the lead role in
the development of bioprocesses [5].
However, novel developments of recombinant protein production, metabolic engineering
and systems biology open a range of new applications of yeasts in the upstream stage
of a bioprocess [5]. In fact, over the
last two decades, the methylotrophic Pichia
pastoris (P. pastoris) has been
established as one of the most frequently used expression systems for recombinant
protein production [6]. The benefits of
this system include growth up to high cell densities quantity on defined minimal
medium, high expression level of heterologous proteins, typical eukaryotic
post-translational modifications, efficient secretion of extracellular proteins and
the presence of the efficient methanol-inducible promoter from alcohol oxidase I
gene (AOX) [7, 8]. Moreover, the P.
pastoris preference for respiratory rather than fermentative
metabolism, even at high cell density processes, prevents the accumulation of
secondary metabolites such as ethanol and acetic acid [7]. Finally, following the recognition of P. pastoris as a GRAS organism by FDA in 2006 [6], the importance of this host as a platform for
biopharmaceuticals production is highlighted. Upon the design of a bioprocess for
recombinant protein production in P. pastoris
under the control of the AOX promoter, a key step is the optimization of the
induction phase since it will directly impact on the yield of the process
[9]. Over the past few years, many
efforts have allowed relevant advances in the development of P. pastoris for the production of MP where significant achievements
were made in order to improve yield and proper folding of these target proteins
[10]. Specifically, chemical
chaperones such as dimethylsulfoxide (DMSO) have been shown to increase the
expression of different G protein-coupled receptors such as the human neuromedin U
subtype II receptor [11], the human
adenosine A2A receptor or the human β2-adrenergic receptor
[12], mostly due to the
up-regulation of the expression of genes involved in membrane lipid components
[10, 13]. In addition, it was also reported that lowering the culture
temperature from 30 to 20°C also leads to an improvement of the expression of MP,
possibly because it slows down protein production, not overloading the translocation
machinery, protein processing or intracellular trafficking [13]. Finally, while the methanol feeding
strategy is one of the most important factors for maximizing heterologous protein
expression, the methanol induction phase may also depend on other operational
conditions (temperature, pH and culture medium), phenotype and specific
characteristics of the heterologous protein produced [14]. In general, the traditional optimization method, commonly
called “one factor/variable at a time”, consists in varying one factor while keeping
the others constant [15, 16] and is extremely time-consuming requiring a
large number of experiments [15]. In
alternative, statistical experimental designs have been widely used and they can be
applied at distinct phases of an optimization process, either for screening
experiments or for searching for the optimal conditions for targeted response(s)
[17]. Overall, response surface
methodology (RSM), which includes factorial design and regression analysis, seeks to
identify and optimize significant factors to maximize the response [18]. On the other hand, artificial neural
networks (ANN) allow estimating relationships between one or more input and one or
more output (also called responses) [16]. In general, ANNs are greater and more accurate modeling
techniques when compared with RSM since they can cope with nonlinearities among the
factor in the prediction of a given response [18]. Indeed, ANNs coupled with design of experiments have been
successfully applied in diverse areas such as the optimization of the culture
conditions [16, 18], pharmaceutics [19] or chromatography [15, 20].

The main aim of this work was to optimize the induction phase for
recombinant MBCOMT production by P. pastoris X33
Mut+ cultures in bioreactor applying central
composite design (CCD) and ANNs.

The structural and functional characterization of a MP depends on the
production of a sufficient amount of active protein, meaning a regular supply of
milligram quantities of the target enzyme [1]. Therefore, to fulfill this requirement, in this work and for
the first time the biosynthesis of MBCOMT by P.
pastoris bioreactor cultures is reported. Initially, in order to
select the most appropriated P. pastoris strain
for MBCOMT biosynthesis, trials at a small-scale in baffled shake-flasks were
carried out. Then, a three-stage bioprocess for the biosynthesis of the target
protein by P. pastoris bioreactor cultures was
implemented and the lengths of the glycerol fed-batch and the methanol induction
phases were optimized.

Moreover, after selecting a set of independent variables associated
with the methanol induction phase that greatly influence the levels of the MBCOMT,
ANN modeling was carried out in order to maximize the biological activity of the
target protein. The massic and volumetric productivities were not incorporated as an
output since the values of those parameters are in strictly dependence on MBCOMT
biological activity [18]. Also, the
biomass levels were evaluated in all assays performed in this work but were not
considered in the optimization and validation procedures as an output, since higher
biomass levels not always lead to higher mass productivities of the target
protein.

Small-scale MBCOMT biosynthesis in P.
pastoris

Membrane-bound catechol-O-methyltransferase biosynthesis was initially carried out in
shake-flasks containing BMGH medium using a Mut+
(X33) and a MutS (KM71H) P.
pastoris strains [21]. Sometimes, an increase in the number of the heterologous
gene can possibly lead to an increase in transcription and translation rate of
the desired gene [22]. In fact,
although opposite results had already been published, there are several examples
including the mouse epidermal growth factor or miniproinsulin in which higher
target gene copy numbers lead to higher titers for P.
pastoris bioprocesses driven by AOX1 promoter [22]. Therefore, upon the transformation
procedure with the target recombinant plasmid, clones from both strains in study
were isolated from plates containing high zeocin concentrations (2 mg/mL).
Following the isolation of these clones from both strains, it was determined the
target gene copy number that was integrated in each strain. Therefore, using the
method previously reported by Nordén and collaborators [23] that takes advantage of the fact that
part of the plasmid pPICZ α, namely the AOX1 TT region is incorporated in the
P. pastoris genome together with the gene
to be expressed. In particular, for the X33 strain, the primer efficiencies were
1.88 and 1.87, respectively for the AOX1 TT and AOX2 PROM primer pairs.
Similarly, for the KM71H strain, the primer efficiencies were 1.91 and 1.94,
respectively, for the AOX1 TT and AOX2 PROM primer pairs. Finally, according the
equation described in the “Methods”, the
target gene copy number introduced in each recombinant strain was determined and
it was found that X33-PICZα-MBCOMT had nine copies of the target plasmid while
the KM71H-PICZα-MBCOMT had ten copies. In fact, Nordén and coworkers
[23] reported with the
aquaporins that colonies isolated from 0.5 mg/mL zeocin could harbor from 4 to
15 plasmids while from 1 mg/mL, as many as 17 heterologous DNA sequences can be
incorporated. Therefore, although the isolation of clones from plates containing
higher antibiotic concentrations doesn’t exclude completely the occurrence of
false positives, the values here reported (9 and 10 copies for the X33 and KM71H
strains, respectively) are in the same order of magnitude. Then, small-scale
fermentation trials were carried out using 0.5% (v/v) methanol and higher
biomass levels were detected for the X33 strain
(OD600 = 7.5) when compared with those obtained for the
KM71H strain (OD600 = 1.8). Similarly, the target enzyme
recovered from the X33 strain presented higher biological activity
(60.25 nmol/h/mg) in comparison to KM71H cells (25.77 nmol/h/mg of protein)
[21]. On the other hand, when
the methanol concentration is lowered from 1 to 0.25% (v/v), similar values for
MBCOMT biological activity were obtained for the X33 (61.73 nmol/h/mg of
protein) and the KM71H (60.62 nmol/h/mg of protein) strains [21]. Specifically, we believe that the
observed differences in these two strains concerning their performance in MBCOMT
biosynthesis seem to be associated with the methanol concentration used for
induction and not for example with the target gene copy number inserted in the
genome since it is similar.

The value previously reported [21] with both P. pastoris
strains for MBCOMT biological activity is higher than those previously reported
by our research group using Brevibacillus
choshinensis as the expression system (48.07 nmol/h/mg of
protein) [24]. In general, for
intracellular expression, it has been reported that it is preferable use
MutS over Mut+P. pastoris strains because of increased
specific yield of heterologous protein [25]. However, as previously reported by Maurer and
collaborators, the volumetric productivity QP is the most plausible target for
optimization in fed-batch processes [26]. Therefore, since the main aim of this work was to
maximize MBCOMT expression irrespective the biomass levels, P. pastoris Mut+ X33 was
chosen for further bioreactor trials since regardless the methanol concentration
used, the expression levels of the target protein were the highest obtained and
they didn’t significantly change when different methanol concentrations are
applied.

Membrane-bound catechol-O-methyltransferase biosynthesis was carried out in mini-bioreactors
(working volume 0.25 L) in modified basal salts medium (BSM) containing
4.35 mL/L trace metal solution (SMT) [27] and the pH was adjusted to 4.7 in order to minimize
precipitation and, consequently, undesired operational problems such as
starvation of nutrients and optical densities measurement interferences
[14]. P. pastoris cultivations in bioreactor were initiated with a
glycerol batch phase (30 g/L glycerol) that ends when glycerol was depleted,
indicated by a sharp increase in the dissolved oxygen (DO) [14]. After this stage, a fed-batch growth on
glycerol [50% (v/v) at 18.54 mL/L/H] during different periods was employed,
followed by the methanol induction phase where P.
pastoris was cultivated on a methanol fed-batch mode. In order to
promote the derepression of the AOX promoter prior to induction, 1 h before
starting the induction phase, methanol was added to the reaction vessel at the
flow-rate later employed in the methanol fed-batch phase.

Preliminary trials were carried out in order to analyze the optimal
period of the glycerol fed-batch phase as well as the optimal duration of the
methanol induction phase that maximize MBCOMT expression. Therefore, keeping
constant the methanol flow-rate (3.6 mL/L/H) in the induction phase, assays with
3, 5 or 7 h glycerol fed-batch phase were carried out. Methanol induction phase
was maintained during 60 h and samples were collected with an interval of 2 h
until 12 h and then every 12 h to follow the MBCOMT expression profile. As
depicted in Fig. 1, the highest MBCOMT
biological activity levels were detected when a 3 h period was applied in the
glycerol fed-batch phase. In addition, concerning to the methanol induction
phase, MBCOMT achieved a maximum expression of 121.0 nmol/h/mg of protein at
12 h of induction, what led us to assume a 3 h glycerol fed-batch period and a
12 h induction period for further experiments. In fact, a shorter induction
period can be greatly advantageous over other previously reported strategies
[27, 28] where induction usually takes more than
48 h, being more time-consuming and laborious. Moreover, the shorter induction
period allows terminating the fermentation before a decrease in the cell’s
physiological activity is observed [29].

Fig. 1

Typical time profile of MBCOMT specific activity
(nmol/h/mg of protein) obtained by P.
pastoris bioreactor cultures using different
periods of the glycerol fed-batch phase with a methanol constant
feed flow-rate at 3.6 mL/L/H (each value represents the mean of
three independent samples).

Following these findings, we evaluated if the expression of the
target protein was significantly affected by the methanol constant flow-rate as
well as the addition of the chemical chaperone DMSO that has been described to
increase the expression levels of some MP [11–13,
30, 31]. Therefore, keeping constant the
operational parameters previously optimized, distinct assays were performed:
with different methanol constant flow rates at 2, 3.6 and 5.2 mL/L/H while
others were performed maintaining the methanol flow-rate at 3.6 mL/L/H and
changing the DMSO concentration [2.5, 5 and 7.5% (v/v)] in the culture according
to what previously described [11–13,
30]. As demonstrated in
Fig. 2a, for the lowest methanol
constant flow-rate (2 mL/L/H), a highest MBCOMT expression level of
158 nmol/h/mg were obtained, contrasting with 120 and 107 nmol/h/mg for 3.6 and
5.2 mL/L/H, respectively. Also, the methanol and the biomass levels at distinct
stages of the induction phase were quantified in these assays, as depicted in
Fig. 2b and Table 1, respectively. In general, for the different
methanol flow-rates applied, the methanol levels increase from 0 to 6 h and then
they decrease until the end of the induction phase. At the early stage of the
induction phase, methanol doesn’t seem to be consumed in a large extent since
P. pastoris cells may be going through a
transition period where they stop consuming glycerol and start to oxidize
methanol. Nevertheless, it is possible to observe that for methanol
constant-flow rates of 3.6 and 5.2 mL/L/H, the concentration of methanol in the
culture broth is higher (near 10 and 12.5 g/L respectively) at 6 h of induction
when compared with the lowest flow-rate employed (1 g/L). Therefore, it is
feasible to assume that using a lower flow rate (2 mL/L/H) allows the
establishment of an appropriated balance between activation of the AOX promoter
and, consequently, production of the target enzyme and accumulation of methanol
in the culture medium that can be responsible for the undesired toxicity, as it
may be happening for 3.6 and 5.2 mL/L/H [14]. Moreover, an optimal ratio of methanol to cell
concentration should be applied [32], otherwise high methanol feeding rates stress the cell
machinery and negatively affect the process performance [32, 33].

Fig. 2

a Analysis of different
methanol flow-rates (without the addition of DMSO) and different
DMSO concentrations (keeping constant the methanol flow-rate at
3.6 mL/L/H) on MBCOMT specific activity (nmol/h/mg of protein)
obtained by P. pastoris
bioreactor cultures. b Time
course analysis of the methanol levels in the above mentioned
assays measured by HPLC-RID. In both experiments, a 3-h period
of the glycerol fed-batch was applied and induction was
conducted during 12 h (each value represents the mean of three
independent samples).

Table 1

Time course profile of the biomass levels (measured as
OD600 nm) obtained in the trials
where the methanol constant feed flow-rate (2, 3.6 and
5.2 mL/L/H) and the DMSO levels added to the culture were
changed, in accordance with the results shown in
Fig. 2b

Time after induction phase (h)

Optical density measurements at
600 nm

Methanol constant feed flow-rate

DMSO concentration

2 mL/L/H

3.6 mL/L/H

5.2 mL/L/H

2.5% (v/v)

5% (v/v)

7.5% (v/v)

3

111.75 ± 1.23

105.19 ± 5.57

116.75 ± 4.42

112.75 ± 4.95

113.44 ± 4.33

104.88 ± 2.47

9

110.32 ± 2.38

106.88 ± 7.95

110.5 ± 3.36

110.19 ± 2.21

114.43 ± 1.17

113.44 ± 0.27

15

111.31 ± 4.68

111.5 ± 9.02

117.38 ± 2.47

116.31 ± 2.21

132.00 ± 7.07

115.06 ± 3.62

On the other hand, when different DMSO concentrations were added to
the P. pastoris cultures, the highest MBCOMT
biosynthesis of 216 nmol/h/mg was detected for 5% (v/v), which represents an
increase of 1.8-fold when compared with the control (without DMSO). Again, the
methanol levels were also quantified in these trials and interestingly, its time
course profile with the addition of 5% (v/v) DMSO conducted with 3.6 mL/L/H of
methanol resembles the profile previously obtained for the 2 mL/L/H methanol
flow rate and not the 3.6 mL/L/H. Following these results, it is reasonable to
think that adjusting the DMSO concentration to the cell needs, the methanol is
more efficiently used what, in a last analysis, leads to an increase in the
biosynthesis of the target protein.

The addition of 5% (v/v) DMSO proved to have a positive effect on
the expression of this particular MP, has been demonstrated previously for G
protein-coupled receptors by other authors [11–13,
30, 31]. Although the mechanism by which DMSO
increases MP expression is not yet fully understood, Murata and collaborators
showed that DMSO induces membrane proliferation through the increase of the
phospholipid content within Saccharomyces
cerevisiae cells [34]. On the other hand, it was also reported that DMSO possess
antioxidant properties, preventing protein oxidation (increase in protein
carbonyl content and decrease in free thiol content) in rat brain homogenates
induced by ferrous chloride/hydrogen peroxide [35]. Therefore, it is likely that the benefits of using DMSO
on the expression of membrane proteins can be associated with the induction of
membrane proliferation or with the reduction of protein oxidation or a
combination of both. Moreover, despite the optimal temperature for growth and
production of proteins in P. pastoris is 30°C
[14], some authors claim that
working at lower temperatures (until 20 to 25°C) may improve the target protein
biosynthesis [36], lower cell lysis
[37] and decrease the
proteolytic activity [38].
Therefore, in this work, the temperature was also included as an independent
process parameter to optimize MBCOMT biosynthesis from P. pastoris and the ranges (20, 25 and 30°C) were selected
according to what has been reported in the literature [14, 37].

According to the results reported in this section and the synergy
observed between methanol flow rate and DMSO concentration in the culture broth,
the most appropriated ranges of the independent variables selected for
performing the experimental design were defined, as shown in Table 2. Finally, a summary of the optimized conditions
for the expression of MBCOMT from P. pastoris
bioreactor methanol-induced cultures is presented in Fig. 3 where the ranges of the independent variables
selected for the ANN modeling are presented as well as the major experimental
conditions selected.

Table 2

Coded levels used for temperature, methanol constant
feed flow-rate and DMSO in the CCD

Input variables

Coded levels

−1

0

1

Temperature (°C)

20

25

30

Methanol constant feed rate
(mL/h/L)

1

2

3

DMSO [% (v/v)]

4

5

6

Fig. 3

Structure of the optimized four-stage bioprocess
implemented in this work for recombinant MBCOMT biosynthesis by
P. pastoris bioreactor
cultures.

Experimental design and artificial neural network modeling

A set of 17 experiments defined by CCD for optimization of the
induction phase for maximizing MBCOMT biosynthesis in P.
pastoris culture are listed in Tables 2 and 3. In general,
lower MBCOMT biological activity levels were detected when the input variables
defined in CCD were at the lowest levels. Specifically, MBCOMT biosynthesis is
maximized at higher methanol constant-flow rate concentrations and when the
concentration of DMSO added is higher. On the other hand, an increased in the
induction temperature coupled to an increase in the other input variables also
lead to an increase in biologically active MBCOMT expression. According to the
ANN modeling results in calibration dataset (DoE experiments)
(Table 3), the predicted maximum for
MBCOMT specific activity (384.8 nmol/h/mg of protein) was achieved at 30°C,
2.9 mL/L/H methanol constant flow-rate and with the addition of 6% (v/v) DMSO.
In general, as previously demonstrated for others MP [11–13, 30,
31], the addition of DMSO to
the culture proved to have a positive effect on MBCOMT expression since over the
model optimization the maximum target protein specific activity is achieved at
higher DMSO concentrations. In addition, the output seems to be maximized when
the methanol constant flow-rate and the induction temperature are close to the
upper values defined in the CCD. This can be explained by the increase in the
biomass levels (data not shown) caused by the increase in the temperature and,
since there is more methanol that is being oxidized by the AOX promoter, the
supply of inducer needs to be higher in order to maintain induction. An ANN
model was developed in order to optimize the induction phase for maximizing
MBCOMT biosynthesis from P. pastoris
bioreactor cultures. The model was calibrated with the experiments defined in
Table 3.

Table 3

List of experiments performed for MBCOMT biosynthesis
from P. pastoris bioreactor
methanol-induced cultures based on CCD and ANN
modeling

Experiment number (ANN model
iterations)

Input variables level

Output

Methanol constant feed flow-rate
(mL/L/h)

Induction temperature (°C)

DMSO concentration [%(v/v)]

Observed

Predicted

DoE

1

1

20

4

126.1

122.2

2

3

20

4

163.9

139.3

3

1

30

4

47.4

97.0

4

3

30

4

188.0

187.1

5

1

20

6

138.0

143.8

6

3

20

6

130.6

139.4

7

1

30

6

151.4

97.5

8

3

30

6

(153.9)

358.1

9

1

25

5

105.3

116.5

10

3

25

5

137.9

134.2

11

2

20

5

115.2

136.8

12

2

30

5

101.1

105.5

13

2

25

4

183.9

197.2

14

2

25

6

222.6

218.5

15

2

25

5

252.5

243.3

16

2

25

5

243.8

243.3

17

2

25

5

230.3

243.3

I

18

1

22.5

6

364.3

343.1

19

1

22.5

6

364.6

343.1

20

1

22.5

6

357.6

343.1

II

21

2.9

30

6

390.6

383.1

22

2.9

30

6

391.5

383.1

III

23

3

30

6

377.1

358.1

24

3

30

6

377.4

358.1

IV

25

2.5

30

6

263.0

258.9

26

2.5

30

6

283.7

258.9

Final validation

27

2.9

30

6

–

384.8

The predicted values of MBCOMT specific activity (nmol/h/mg
of protein) are those obtained in the last optimization iteration.
Observed outputs in parentheses represent the model
outliers.

Modeling of the methanol induction phase using artificial neural
network

The ANN model was applied for the optimization of the induction
phase for MBCOMT biosynthesis in P. pastoris
bioreactor cultures using a stepwise process until the maximum MBCOMT biological
activity was achieved. Four iterations were required to achieve the maximum
MBCOMT specific activity (384.8 nmol/h/mg of protein) under the optimal
conditions [30°C, 2.9 mL/L/H methanol constant flow-rate and 6% (v/v) DMSO] and
to validate the model with new experiments. In the end, an improvement of
1.53-fold over the best conditions performed in the DoE step (see experiment 15,
Table 3) was achieved while an
improvement of 6.4-fold over the small-scale biosynthesis in baffled
shake-flasks was achieved.

The obtained ANN model is mostly unbiased because the slope and
R2 of the fitting between the measured and
predicted output were close to 1 (0.9064 and 0.97161, respectively) (see
Fig. 4). In Fig. 5 are depicted the contour plots obtained from
the ANN model for two combinations between the three operational conditions in
study. The modeling results showed that the MBCOMT specific activity is
sensitive to the operational conditions. The ANN parameters for the final
validation model are presented in Additional file 1.

Fig. 4

ANN modeling results of MBCOMT specific activity for the
last optimization steps. Blue
circle, red
circles and green
triangles represent data from the CCD, outliers
and from model optimization.

Fig. 5

Contour plots of MBCOMT specific activity as function of
induction temperature, methanol constant flow-rate and DMSO
concentration: a modeling
results of MBCOMT specific activity as function of DMSO
concentration and methanol constant flow-rate for the last
optimization step. b Modeling
results of MBCOMT specific activity as function of induction
temperature and methanol constant flow-rate for the last
optimization step.

Bioprocess monitoring at the optimal conditions estimated by the ANN
model

At the optimal conditions estimated by the ANN model [30°C,
2.9 mL/L/H methanol constant flow-rate and 6% (v/v) DMSO], the carbon source
levels as well as the biomass levels and the number of viable/depolarized cells
were analyzed, as depicted in Fig. 6. In
what concerns to the P. pastoris growth, a
marked increase in OD600 was detected between the end of
the batch phase and the fed-batch growth of glycerol and it keeps increasing
until the end of the induction phase with a value near 123 units of
OD600. The methanol and glycerol levels were
quantified using a HPLC with refractive index detection and it was verified that
the glycerol concentration also increases during the fed-batch glycerol phase,
despite the higher accumulation of biomass during this stage. On the other hand,
a low consumption of methanol was verified between the second and the third
hours of the glycerol fed-batch phase since we consider that the consumption of
glycerol is preferred over the methanol. On the other hand, at the end of the
induction phase, almost no methanol was detected since P. pastoris cells are oxidizing it all, what can be indicating
that the AOX promoter is highly active. Finally, the flow cytometry analysis led
us to conclude that the changes introduced at the second hour of the glycerol
fed-batch phase (namely the shift to the induction temperature, the addition of
DMSO and the initiation of the methanol flow-rate) did not altered significantly
the number of viable cells (94.8% compared to 95.4%) in culture. Furthermore, at
the end of the induction phase, approximately 90% of viable cells were obtained,
a value that is acceptable and is in accordance with P.
pastoris bioprocesses that include the AOX promoter with a 12 h
induction period [39].

Fig. 6

Time course analysis of biomass levels, carbon sources
concentrations and number of healthy P.
pastoris cells at different stages of the optimal
point estimated by the ANN model [30°C, 2.9 mL/L/H methanol
constant flow-rate and 6% (v/v) DMSO]. a Biomass levels measured by spectrophotometric
determination at 600 nm and methanol and glycerol levels
measurements by HPLC with RID; (each value represents the mean
of three independent samples). b Dot plots of green fluorescence of cells (BOX,
x-axis) plotted against red fluorescence (PI, y-axis) obtained
with cell samples taken at different stages of the optimum point
retrieved from the ANN modelling. Three main subpopulations of
cells can be distinguished corresponding to: healthy cells, no
staining; cells with depolarized membranes, stained with BOX;
and cells with permeabilized membranes, stained with PI. A total
of 10,000 events were collected for these analysis. The
variation on the percentage of healthy cells at different stages
of the bioprocess is depicted in the graph. Each experiment was conducted in
duplicate.

To our best knowledge, this is the first systematic study where the
interaction between two commonly studied operational parameters (induction
temperature and methanol flow rate) and the addition of chemical chaperones
(specifically the DMSO) are successfully reported to optimize MP expression by
P. pastoris bioprocesses using ANN
modeling.

Membrane-bound catechol-O-methyltransferase biosynthesis in a highly biological active form was
successfully attained for the first time by P.
pastoris bioreactor cultures under the control of the AOX promoter.
The ANN model was able to describe the effects of temperature, DMSO concentration
and methanol flow-rate on MBCOMT specific activity, as shown by the good fitness
between the predicted and measured values. At the optimal conditions estimated by
the ANN model [30°C, 2.9 mL/L/H methanol constant flow-rate and 6% (v/v) DMSO], a
1.58-fold increase was obtained for MBCOMT specific activity (384.8 nmol/h/mg of
protein) over the highest value achieved in the experimental design while an
improvement of 6.4-fold was found over the small-scale biosynthesis in baffled
shake-flasks. Furthermore, in these conditions, almost 90% of viable cells were
obtained at the end of the induction phase, indicating that the implemented
experimental strategy allowed maintaining the viability of P. pastoris cultures. This experimental procedure highlighted the
potential of chemical chaperones such as DMSO play to improve the yield of
recombinant membrane proteins with a different topology than G-coupled receptors. In
addition, this is the first systematic study where the interaction between two
commonly studied operational parameters (induction temperature and methanol flow
rate) and the addition of chemical chaperones (specifically the DMSO) were
successfully reported for the optimization of P.
pastoris bioprocesses using ANN models. The experimental strategy
developed in this work shows that the manipulation of fermentation conditions
coupled with the addition of specific molecules can open new perspectives in the
optimization of Pichia pastoris bioprocesses for
recombinant membrane protein biosynthesis.

Small-scale MBCOMT biosynthesis in Pichia
pastoris

Easy select expression kit for expression of recombinant proteins
using pPICZα in P. pastoris X33 cells
(Invitrogen, Carlsbad, CA, USA) was used for the expression of human MBCOMT in
its native form and the process was carried out according to manufacturer’s
instructions. Specifically, as the correct membrane protein targeting to the
membrane is usually enhanced when secretion signals are used [41], the pPICZα expression vector was
employed for expressing MBCOMT expression as it contains the alpha mating factor
from Saccharomyces cerevisiae. For more
details about the construction of the expression vector, please refer to
Additional file 2. Subsequently,
before conducting the initial trials for MBCOMT biosynthesis at a small-scale,
the recombinant plasmid was sequenced in order to confirm the presence of the
full sequence of the MBCOMT protein. In fact, after the analysis of the obtained
results (Please refer to Additional file 3) concerning the sequencing analysis, it was possible to
conclude that the recombinant plasmid contains the full sequence of the MBCOMT
protein.

Recombinant hMBCOMT biosynthesis at a small-scale was carried out
according to the following protocol [21]: cells containing the expression construct were grown at
30°C in YPD plates. A single colony was inoculated in 50 mL of BMGH medium in
250 mL shake flasks. Cells were grown at 30°C and 250 rpm overnight when the
OD600 typically reached 6.0. Subsequently, since the
inoculation volume was fixed to achieve an initial OD600
of 1, an aliquot of the fermentation in the medium BMGH was collected and
centrifuged at room temperature during 5 min. After centrifuging the cells and
ensuring that all glycerol was removed, the cells were resuspended in the
induction medium and added to 500 mL shake-flasks to a total volume of 100 mL.
The fermentations were carried out during 120 h at 30°C and 250 rpm, the cells
were harvested by centrifugation (1,500×g,
10 min, 4°C) and stored frozen at −20°C until use.

Fed-batch Pichia pastoris bioreactor
cultures

A single colony was used to inoculate a 100 mL BMGH seed culture in
500 mL shake-flasks and it was grown overnight at 250 rpm and 30°C. This culture
was grown to an OD600 of 6 and used to inoculate 250 mL
of modified basal salts medium (BSM) [26] containing 4.35 mL/L SMT [27] and 200 µg/mL zeocin in a 0.75 L (total volume)
bioreactor (Infors HT, Switzerland). The bioreactors were operated with strictly
controlled parameters including pH, temperature, airflow, agitation and
dissolved oxygen. The pH was set at 4.7 and the DO set point was 20%. The
temperature was 28°C in the batch phase while the pH was set at 4.7 during the
entire assay and maintained by the addition of 12.5% (v/v) ammonium hydroxide
and 0.75 M sulfuric acid. Foaming was controlled manually by the addition of the
antifoam agent antifoam A (Sigma-Aldrich, St. Louis, MO, USA). The dissolved
oxygen concentration was maintained at 20% by automatic adjustment of the
airflow (maximum gas flow-rate used was 2 vvm) and the agitation rate (maximum
agitation rate was fixed in 950 rpm). Preliminary trials were carried out in
order to determine the best strategy for the biosynthesis of MBCOMT from
P. pastoris. Therefore, unless otherwise
stated, the optimized strategy (see Fig. 3) consisted of a glycerol batch phase that was carried out
at 28°C until all glycerol had been consumed, indicated by a DO spike to 45%.
Then, a glycerol fed-batch phase was initiated with a constant feed rate of
18.54 mL/L of 50% (v/v) glycerol containing 12 mL/L of SMT during 3 h. After 2 h
elapsed, a transition phase was initiated through the addition of a 100%
methanol at a constant feed rate, the temperature was changed for the induction
temperature and the DMSO was added to the reaction vessel. The constant methanol
feed rate, the temperature and the DMSO concentration were defined according to
the experimental design. Then, after 3 h elapsed, the induction phase was
maintained during additional 12 h using methanol as sole carbon and energy
source. The whole system was controlled by IRIS software (Infors HT,
Switzerland) and, in particular, the addition of feed medium was achieved using
peristaltic pumps that were automatically controlled through a feeding profile
previously programmed.

Experimental design

A CCD with three levels and three factors was employed for the
experimental design. The factors and levels for the optimization of MBCOMT
specific activity were conditions associated with the fermentation process,
namely, the temperature (20, 25 and 30°C), the 100% (v/v) methanol constant feed
rate (1, 2 and 3 mL/h/L of culture) and the DMSO concentration [4, 5 and 6%
(v/v)]. Table 2 lists the fermentation
conditions parameters used in the experimental design and in model development
and optimization by ANN.

Artificial neural network

A feed-forward artificial neural network was applied to predict the
MBCOMT specific activity as function of the fermentation conditions
(temperature, methanol constant flow-rate and DMSO concentration). The ANN
models were implemented in MATLAB™ using the Neural Network Toolbox. The ANN
structure included an input layer with three neurons (one for each input
variables), an output layer with one neuron (MBCOMT specific activity) and one
hidden layer with two neurons (3/2/1). Therefore, the resulting model contains a
total of 11 parameters. The transfer functions of the input and output layers,
the mathematically representation of the output function and the ANN structure
were described elsewhere [18]. The
ANN structure was built using the “newff” function. ANN was trained with the
Levenberg–Marquardt back-propagation function, up to 1,000 epochs, using the
“train” function. The learning rate and the ratio to increase learning rate were
set at 0.01 and 1.05, respectively.

Flow cytometry assays

Cellular viability was assessed during the fermentation runs.
Samples were collected at specific periods and analyzed by flow cytometry
following the protocol described by Hyka and co-authors [39]. Briefly, the samples
OD600 was measured, a dilution with PBS buffer was
prepared to obtain a final OD600 of 0.1 and appropriated
volumes of PI and BOX were added in order to attain final concentrations of 10
and 2 mg/L, respectively. The samples were incubated for 15 min at room
temperature in the dark, centrifuged for 10 min at 1,500 rpm, resuspended in PBS
and sonicated in the “hotspot” during 1 min. The samples were analyzed on a BD
Biosciences FACSCalibur (Becton–Dickinson GmbH, Heidelberg, Germany),
acquisition was performed with CellQuest™ Pro Software Light scatter
measurements and fluorescence was collected in two optical channels, FL1
(515–545 nm, BOX) and FL4 (>670 nm, PI). Threshold was set on SSC to exclude
noise, other particles and debris while sample acquisition was operated at flow
rate of no more than 300 events per second and a total of 10,000 cells were
gated and analyzed in each sample. Data analysis was performed using FCS Express
Version 3 Research Edition (De Novo Software™, Los Angeles, CA, USA). The
samples were incubated 30 min at 70°C to provide positive staining controls,
thereby allowing detection of dead cells and were incubated 2 min at 60°C in
order to provide the identification of three subpopulations.

HPLC analytical methods

The methylating efficiency of recombinant MBCOMT was evaluated by
measuring the amount of metanephrine using epinephrine as substrate and as
previously described [42]. Briefly,
the MBCOMT lysates were incubated at 37°C for 15 min, using epinephrine as
substrate and the reaction was stopped with 2 M of perchloric acid. Then, after
processing the samples [42], the
metanephrine levels in the samples were determined using HPLC with
electrochemical detection in a coulometric mode, as previously described
[43]. On the other hand, the
levels of glycerol and methanol in the culture broth were quantified using a
HPLC coupled to a 1260 Infinity Refractive Index Detector (Agilent, Santa Clara,
CA, USA), according to what was previously described [21]. The chromatographic separation was
achieved on a cation-exchange analytical column Agilent Hi-Plex H (300 × 7.7 mm
i. d.; 8 µm) and the analysis was performed at 65°C with a flow rate of
0.6 mL/min using isocratic elution with 0.005 M
H2SO4. The samples were
centrifuged at 6,000 rpm for 10 min and the supernatant was filtered prior the
injection through a 0.22 µm cellulose-acetate filter.

Determination of copy number by qPCR

The recombinant gene dosage present in the plasmid pPICZα-hMBCOMT
introduced into the strains X33 and KM71H was determined according to the method
reported by Nordén and collaborators [23]. Initially, gDNA was extracted from untransformed
colonies of X33 and KM71H P. pastoris strains
as well as from the X33 and KM71H transformants using the Wizard SV Genomic DNA
Purification System (Promega, Madison, USA) supplemented with zymolyase.
Briefly, for internal standardization, a primer pair—PpAOX2_Prom_FW and
PpAOX2_Prom_RV (5′-GACTCTGATGAGGGGCACAT-3′ and 5′-TTGGAAACTCCCAACTGTCC-3′,
respectively)—was used that amplifies a stretch of the AOX2 promoter sequence,
which is present as one copy in P. pastoris
genome [23]. Then, in order to
determine the number of recombinant gene sequences, it was designed another
primer pair—PpAOX1_TT_FW and Pp_AOX1_TT_RV (5′-TGGGCACTTACGAGAAGACC-3′ and
5′-GCAAATGGCATTCTGACATC-3′, respectively)—that is directed towards the 3′TT
sequence of the AOX1 gene, which is also present in the pPICZ and also in the
pPICZ α plasmids and is integrated together with the gene of interest
[23]. The mean efficiency (E)
of the two primer pairs was determined according to the serial dilution method
using gDNA extracted from both untransformed strains, starting from 100 ng. For
each reaction, 10 ng of template were used and the thermal cycler was programmed
to perform an initial incubation step at 95°C during 10 min and then 40 cycles
of: 15 s at 95°C, 30 s at 60°C, 30 s at 72°C. According to what was previously
described by Nordén and collaborators [23], the average copy number was calculated with the
following equation:

where Ravg is the average copy number, E the mean primer efficiency,
Ct the critical take off cycle, sample the clone in study, reference the strain
used (X33 or KM71H), A the AOX1-TT, B the AOX2 promoter. Finally, in order to
obtain the MBCOMT copy number, the AOX1 TT copy number was subtracted by 1 to
compensate for the endogenous AOX1 TT sequence.

Authors’ contributions

AQP carried out all the experimental procedures and wrote the manuscript.
AQP, LMM, JAQ and LAP designed the study. LMM helped to perform the experimental
procedures. JMLD carried out the ANN modeling. JMLD, MJB, JAQ and LAP contributed to
drafting the manuscript. JAQ and LAP were, respectively, co-supervisor and
supervisor of the project and were responsible for revising the manuscript. All
authors read and approved the final manuscript.

Acknowledgements

This research was supported by University of Beira Interior-Health
Sciences Research Centre (CICS) and FCT (Portuguese Foundation for Sciences and
Technology) by the project “EXPL/BBB478/BQB/0960/2012” and COMPETE:
FCOMP-01-0124-FEDER-027563. A.Q. Pedro acknowledges a doctoral fellowship
(SFRH/BD/81222/2011) from FCT and L. M. Martins a fellowship from the project
PTDC/EBB-BIO/114320/2009. The authors also acknowledge the program COMPETE, the
FCT project (Pest-C/SAU/UI0709/2011). Finally, the authors would like to
acknowledge Filomena Silva and Carlos Gaspar for the valuable help with the flow
cytometry assays.

Compliance with ethical guidelines

Competing interests The authors declare that
they have no competing interests.

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